A Taxonomy for Quality in Simulation-Based Development and Testing of Automated Driving Systems

Abstract

Ensuring the quality of automated driving systems is a major challenge the automotive industry faces. In this context, quality defines the degree to which an object meets expectations and requirements. Especially, highly automated and automated vehicles at SAE levels 4 and 5 will be expected to operate safely in various contexts and complex situations without misconduct. Thus, a systematic approach is needed to show their safe operation. A way to address this challenge is simulation-based testing, as pure physical testing is not feasible for automated driving at levels 4 and 5 since several billion kilometers of driving are necessary. During simulation-based testing, the data used to evaluate the actual quality of an automated driving system are generated using a simulation. However, to rely on these simulation data, the overall simulation, including its simulation models, must provide a certain quality level. This quality level depends on the intended purpose for which the generated simulation data should be used. Therefore, three categories of quality can be considered: simulation quality (e.g., reliable simulation tool), quality of the automated driving system (e.g., handling of dangerous situations), and scenario quality (e.g., scenario criticality). Quality must be determined and evaluated in various process steps in developing and testing automated driving systems, the overall simulation, and the simulation models used for the simulation. In this paper, we propose a conceptual taxonomy to better understand quality in the development and testing process to have a clear separation and insight where further testing is needed – both in terms of automated driving systems and simulation, including their simulation models and scenarios used for testing.

Keywords

• Automated driving
• criticality
• metrics
• PEGASUS family
• quality
• scenario